def YOLOv4_more_tiny(input_layer, NUM_CLASS):
route_1, conv = backbone.cspdarknet53_tiny(input_layer)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv_mbbox = common.convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False) #256
return [conv_mbbox]
def YOLOv4_tiny(input_layer, NUM_CLASS):
route_1, conv = backbone.cspdarknet53_tiny(input_layer)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv_lobj_branch = common.convolutional(conv, (3, 3, 256, 512))
conv_lbbox = common.convolutional(conv_lobj_branch, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.upsample(conv)
conv = tf.concat([conv, route_1], axis=-1)
conv_mobj_branch = common.convolutional(conv, (3, 3, 128, 256))
conv_mbbox = common.convolutional(conv_mobj_branch, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
return [conv_mbbox, conv_lbbox]